The DOI matches its Crossref record; 90 of the 90 references it cites were found in a registry or an index.
Systematic review of smartphone-based passive sensing for health and wellbeing
Open accessAbstract
Objective To review published empirical literature on the use of smartphone-based passive sensing for health and wellbeing.Material and methods A systematic review of the English language literature was performed following PRISMA guidelines. Papers indexed in computing, technology, and medical databases were included if they were empirical, focused on health and/or wellbeing, involved the collection of data via smartphones, and described the utilized technology as passive or requiring minimal user interaction.Results Thirty-five papers were included in the review. Studies were performed around the world, with samples of up to 171 (median n = 15) representing individuals with bipolar disorder, schizophrenia, depression, older adults, and the general population. The majority of studies used the Android operating system and an array of smartphone sensors, most frequently capturing accelerometry, location, audio, and usage data. Captured data were usually sent to a remote server for processing but were shared with participants in only 40% of studies. Reported benefits of passive sensing included accurately detecting changes in status, behavior change through feedback, and increased accountability in participants. Studies reported facing technical, methodological, and privacy challenges.Discussion Studies in the nascent area of smartphone-based passive sensing for health and wellbeing demonstrate promise and invite continued research and investment. Existing studies suffer from weaknesses in research design, lack of feedback and clinical integration, and inadequate attention to privacy issues. Key recommendations relate to developing passive sensing strategies matching the problem at hand, using personalized interventions, and addressing methodological and privacy challenges.Conclusion As evolving passive sensing technology presents new possibilities for health and wellbeing, additional research must address methodological, clinical integration, and privacy issues. Doing so depends on interdisciplinary collaboration between informatics and clinical experts.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Multimodal and intensive sampling data streams (for example, experience sampling and passive measures gathered through smartphones and other electronic devices) used in some MBI studies133 and mental health research broadly134,135 might be particularly promising inputs for machine learning models, given that these models can often handle many correlated predictors136.”
From A common factors perspective on mindfulness-based interventions (2022) · cites this paper as [134]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Preferred Reporting Items for Systematic Reviews and Meta-Analyses: the PRISMA statement
Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., & for the PRISMA Group (2009) · BMJ · doi:10.1136/bmj.b2535 · Cited by 83,823
Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement
Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., & The PRISMA Group (2009) · PLoS Medicine · doi:10.1371/journal.pmed.1000097 · Cited by 64,042
Preferred reporting items for systematic reviews and meta-analyses: the PRISMA Statement.
(2009) · PubMed Central · Cited by 44,859
The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970
The Elements of Statistical Learning: Data Mining, Inference, and Prediction
Lu, Z. Q. J. (2010) · Journal of the Royal Statistical Society Series A (Statistics in Society) · doi:10.1111/j.1467-985x.2010.00646_6.x · Cited by 12,912
The Computer for the 21st Century
Weiser, M. (1991) · Scientific American · doi:10.1038/scientificamerican0991-94 · Cited by 10,017
Machine learning: Trends, perspectives, and prospects
Jordan, M. I., & Mitchell, T. M. (2015) · Science · doi:10.1126/science.aaa8415 · Cited by 9,955
Falls in older people: epidemiology, risk factors and strategies for prevention
Rubenstein, L. Z. (2006) · Age and Ageing · doi:10.1093/ageing/afl084 · Cited by 3,310
The Internet of Things for Health Care: A Comprehensive Survey
Islam, S. M. R., Kwak, D., Kabir, M. H., Hossain, M., & Kwak, K.-S. (2015) · IEEE Access · doi:10.1109/access.2015.2437951 · Cited by 3,199
The Role of Actigraphy in the Study of Sleep and Circadian Rhythms
Ancoli-Israel, S., Cole, R., Alessi, C., Chambers, M., Moorcroft, W., & Pollak, C. P. (2003) · Sleep · doi:10.1093/sleep/26.3.342 · Cited by 2,751
Research works printed but not linked: 2
- Smartphone, New Oxford American Dictionary: Oxford University Press, 2017.
- Microphone, New Oxford American Dictionary: Oxford University Press, 2017.
Cited by
Sorted by citations
Items 101–200 of 406
Smart Sensors for Health Research and Improvement
Garatva, P., Terhorst, Y., Meßner, E.-M., Karlen, W., Pryss, R. C., & Baumeister, H. (2022) · Studies in neuroscience, psychology and behavioral economics · doi:10.1007/978-3-030-98546-2_23 · Cited by 26
Artificial intelligence to aid the detection of mood disorders
Cummins, N., Matcham, F., Klapper, J., & Schuller, B. W. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-817133-2.00010-0 · Cited by 25
Assi, K. C., Meegahapola, L., Droz, W., Kun, P., GÖTZEN, A. D., Bidoglia, M., Stares, S., GASKELL, G. D., Chagnaa, A., Ganbold, A., et al. (2023) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581190 · Cited by 25
Ghorbani, F., Ahmadi, A., Kia, M., Rahman, Q. A., & Delrobaei, M. (2023) · Sensors · doi:10.3390/s23052673 · Cited by 25
Amin, R., Schreynemackers, S., Oppenheimer, H., Petrovic, M., Hegerl, U., & Reich, H. (2025) · Journal of Medical Internet Research · doi:10.2196/57418 · Cited by 26
Langener, A. M., Stulp, G., Kas, M. J., & Bringmann, L. F. (2023) · JMIR Mental Health · doi:10.2196/42646 · Cited by 28
Kruzan, K. P., Ng, A., Stiles‐Shields, C., Lattie, E. G., Mohr, D. C., & Reddy, M. C. (2023) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581209 · Cited by 24
Frank, A. C., Li, R., Peterson, B. S., & Narayanan, S. S. (2023) · JMIR Mental Health · doi:10.2196/45572 · Cited by 27
Recent Advances in Vehicle Driver Health Monitoring Systems
Melders, L., Šmigins, R., & Birkavs, A. (2025) · Sensors · doi:10.3390/s25061812 · Cited by 23
Mental health ubiquitous monitoring supported by social situation awareness: A systematic review
de Moura, I. R., Teles, A. S., Silva, F., Viana, D., Coutinho, L., Barros, F., & Endler, M. (2020) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2020.103454 · Cited by 25
Ethical Dimensions of Digital Phenotyping Within the Context of Mental Healthcare
D’Alfonso, S., Coghlan, S., Schmidt, S., & Mangelsdorf, S. N. (2024) · Journal of Technology in Behavioral Science · doi:10.1007/s41347-024-00423-9 · Cited by 24
Terhorst, Y., Knauer, J., Philippi, P., & Baumeister, H. (2024) · Journal of Medical Internet Research · doi:10.2196/51875 · Cited by 23
Smart Sensing Enhanced Diagnostic Expert Systems
Terhorst, Y., Knauer, J., & Baumeister, H. (2022) · Studies in neuroscience, psychology and behavioral economics · doi:10.1007/978-3-030-98546-2_24 · Cited by 22
Ruyobeza, B., Grobbelaar, S. S. S., & Botha, A. (2022) · Systematic Reviews · doi:10.1186/s13643-022-02033-z · Cited by 24
Swain, V. D., Gao, L., Mondal, A., Abowd, G. D., & Choudhury, M. D. (2024) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642716 · Cited by 24
Swain, V. D., & Saha, K. (2024) · Annual Symposium on Human-Computer Interaction for Work (CHIWORK) · doi:10.1145/3663384.3663401 · Cited by 25
Bellini, D., Mascia, M. L., Conti, R., & Penna, M. P. (2025) · BMC Public Health · doi:10.1186/s12889-024-21269-z · Cited by 25
Comparing ecological momentary assessment to sensor-based approaches in predicting dietary lapse
Crochiere, R. J., Zhang, F., Juarascio, A. S., Goldstein, S. P., Thomas, J. G., & Forman, E. M. (2021) · Translational Behavioral Medicine · doi:10.1093/tbm/ibab123 · Cited by 21
Designing daily-life research combining experience sampling method with parallel data
Velozo, J. D. C., Habets, J., George, S. V., Niemeijer, K., Minaeva, O., Hagemann, N., Herff, C., Kuppens, P., Rintala, A., Vaessen, T., et al. (2022) · Psychological Medicine · doi:10.1017/s0033291722002367 · Cited by 20
Röcke, C., Luo, M., Bereuter, P., Katana, M., Fillekes, M. P., Gehriger, V., Sofios, A., Martin, M., & Weibel, R. (2023) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.1011177 · Cited by 21
Materia, F. T., & Smyth, J. M. (2023) · Internet Interventions · doi:10.1016/j.invent.2023.100702 · Cited by 22
On-Body Sensor Positions Hierarchical Classification
Sang, V. N. T., Yano, S., & Kondo, T. (2018) · Sensors · doi:10.3390/s18113612 · Cited by 20
Stuijt, D. G., van Doeveren, E. E. M., Kos, M., Eversdijk, M., Bosch, J. J., Bins, A. D., Bak, M., & van Oijen, M. G. H. (2024) · JCO Clinical Cancer Informatics · doi:10.1200/cci.24.00092 · Cited by 21
Emotion Forecasting: A Transformer-Based Approach
Paz-Arbaizar, L., López‐Castromán, J., Artés-Rodrı́guez, A., Olmos, P. M., & Ramírez, D. (2025) · Journal of Medical Internet Research · doi:10.2196/63962 · Cited by 20
Digital Tools—Regulatory Considerations for Application in Clinical Trials
Gelis, L., Stoeckert, I., & Podhaisky, H.-P. (2023) · Therapeutic Innovation & Regulatory Science · doi:10.1007/s43441-023-00535-z · Cited by 19
Wiedermann, M., Rose, A. H., Maier, B. F., Kolb, J. J., Hinrichs, D., & Brockmann, D. (2023) · PNAS Nexus · doi:10.1093/pnasnexus/pgad223 · Cited by 21
Schoedel, R., & Mehl, M. R. (2024) · Cambridge University Press eBooks · doi:10.1017/9781009170123.014 · Cited by 22
National Institutes of Health Support of Digital Health Behavior Research
Riley, W. T., Oh, A., Aklin, W. M., & Wolff‐Hughes, D. L. (2019) · Health Education & Behavior · doi:10.1177/1090198119866644 · Cited by 19
Wang, Y., Ren, X., Liu, X., & Zhu, T. (2021) · JMIR mhealth and uhealth · doi:10.2196/19046 · Cited by 23
Using mobile devices as scientific measurement instruments: Reliable android task scheduling
González‐Pérez, A., Matey-Sanz, M., Granell, C., & Casteleyn, S. (2022) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2022.101550 · Cited by 19
Gopalakrishnan, A., Venkataraman, R., Gururajan, R., Zhou, X., & Genrich, R. (2022) · PeerJ Computer Science · doi:10.7717/peerj-cs.1042 · Cited by 21
Caporusso, E., Melillo, A., Perrottelli, A., Giuliani, L., Marzocchi, F. F., Pezzella, P., & Giordano, G. M. (2025) · Frontiers in Behavioral Neuroscience · doi:10.3389/fnbeh.2025.1543005 · Cited by 19
Anaduaka, U. S., Oladosu, A. O., Katsande, S., Frempong, C. S., & Awuku-Amador, S. (2025) · BMJ Mental Health · doi:10.1136/bmjment-2024-301445 · Cited by 21
Bennett, C. C., Ross, M. K., Baek, E., Kim, D., & Leow, A. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00741-3 · Cited by 19
Kothare, P. A., Jadhav, P. R., Gupta, P. K., Harrelson, J. C., & Dickmann, L. J. (2018) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.1100 · Cited by 18
Berrocal, A., Manea, V. C., Masi, A. D., & Wac, K. (2020) · Procedia Computer Science · doi:10.1016/j.procs.2020.07.033 · Cited by 18
Sensor technology implementation for research, treatment, and assessment of eating disorders
Levinson, C. A., Christian, C., Kinkel‐Ram, S. S., Brosof, L. C., & Williams, B. M. (2019) · International Journal of Eating Disorders · doi:10.1002/eat.23120 · Cited by 19
Smartphones for musculoskeletal research – hype or hope? Lessons from a decennium of mHealth studies
Beukenhorst, A. L., Druce, K. L., & Cock, D. D. (2022) · BMC Musculoskeletal Disorders · doi:10.1186/s12891-022-05420-8 · Cited by 18
The Role of a Smart Health Ecosystem in Transforming the Management of Chronic Health Conditions
Nourse, R. A., Dingler, T., Kelly, J. T., Kwaśnicka, D., & Maddison, R. (2023) · Journal of Medical Internet Research · doi:10.2196/44265 · Cited by 17
Pryss, R. C., John, D. T., Reichert, M., Hoppenstedt, B., Schmid, L., Schlee, W., Spiliopoulou, M., Schobel, J., Kraft, R., Schickler, M., et al. (2019) · IEEE International Conference on Information Reuse and Integration (IRI) · doi:10.1109/iri.2019.00061 · Cited by 17
van Heerden, A. C., Wassenaar, D. R., Essack, Z., Vilakazi, K., & Kohrt, B. A. (2019) · Journal of Empirical Research on Human Research Ethics · doi:10.1177/1556264619881334 · Cited by 16
Emmenis, M. V., Jamison, J. Q., Kassavou, A., Hardeman, W., Naughton, F., A’Court, C., Sutton, S., & Eborall, H. (2022) · BMJ Open · doi:10.1136/bmjopen-2021-053183 · Cited by 13
Asynchronous Technologies in Mental Health Care and Education
Lagera, P. G. D., Chan, S. R., & Yellowlees, P. (2023) · Current Treatment Options in Psychiatry · doi:10.1007/s40501-023-00286-6 · Cited by 15
Hamilton, J. L., Dreier, M. J., Caproni, B., Fedor, J., Durica, K. C., & Low, C. A. (2024) · Journal of Technology in Behavioral Science · doi:10.1007/s41347-024-00443-5 · Cited by 19
Digitale Phänotypisierung in der Psychologie – ein Quantensprung in der psychologischen Forschung?
Baumeister, H., Garatva, P., Pryss, R. C., Ropinski, T., & Montag, C. (2022) · Psychologische Rundschau · doi:10.1026/0033-3042/a000609 · Cited by 15
Lisowska, A., Szymon Wilk, A., & Mor Peleg, B. (2022) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2022.104276 · Cited by 15
A Survey of Passive Sensing for Workplace Wellbeing and Productivity
Nepal, S. K., Martinez, G. J., Pillai, A., Saha, K., Mirjafari, S., Swain, V. D., Xu, X., Audia, P. G., Choudhury, M. D., Dey, A. K., et al. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-93845-0_29 · Cited by 15
Gabb, V. G., Blackman, J., Morrison, H. D., Li, H., Kendrick, A. H., Turner, N. L., Greenwood, R., Biswas, B., Heslegrave, A., & Coulthard, E. J. (2025) · JMIR Aging · doi:10.2196/72824 · Cited by 14
Clinical Outcomes of Passive Sensors in Remote Monitoring: A Systematic Review
Rama, E., Zuberi, S. J., Aly, M., Askari, A., & Iqbal, F. M. (2025) · Sensors · doi:10.3390/s25113285 · Cited by 16
Vital Block and Vital Sign Server for ECG and Vital Sign Monitoring in a Portable u-Vital System
Bae, T.-W., Kwon, K.-K., & Kim, K. H. (2020) · Sensors · doi:10.3390/s20041089 · Cited by 15
Public Attitudes to Digital Health Research Repositories: Cross-sectional International Survey
Vilaza, G. N., Coyle, D., & Bardram, J. E. (2021) · Journal of Medical Internet Research · doi:10.2196/31294 · Cited by 14
García, A., Balasubramanian, V., Lee, J., Gardner, R. M., Gummidipundi, S. E., Hung, G., Ferris, T., Cheung, L., Granger, C. B., Kowey, P. R., et al. (2022) · Journal of Biopharmaceutical Statistics · doi:10.1080/10543406.2022.2080698 · Cited by 15
Dong, G., Boukhechba, M., Shaffer, K. M., Ritterband, L. M., Gioeli, D. G., Reilley, M. J., Le, T. M., Kunk, P. R., Bauer, T. W., & Chow, P. I. (2021) · Journal of Healthcare Informatics Research · doi:10.1007/s41666-021-00098-4 · Cited by 14
User-Centered Design of a Mobile App to Support Peer Recovery in a Clinical Setting
Pater, J. A., Phelan, C., Cornet, V. P., Ahmed, R., Colletta, S., Hess, E. P., Kerrigan, C., & Toscos, T. (2021) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3449186 · Cited by 13
Self-citationChan, H. F., Cheng, Z., Mendolia, S., Paloyo, A. R., Tani, M., Proulx, D., Savage, D. A., & Torgler, B. (2024) · Scientific Reports · doi:10.1038/s41598-024-51854-6 · Cited by 13
Pichayapinyo, P., Sompopcharoen, M., Thiangtham, W., Sillabutra, J., Meekaew, P., Bureerat, B., & Somboonkaew, A. (2024) · BMC Family Practice · doi:10.1186/s12875-024-02621-z · Cited by 13
Rogan, J., Firth, J., & Bucci, S. (2024) · Health Expectations · doi:10.1111/hex.70116 · Cited by 14
Sensing and Changing Human Behavior for Workplace Wellness
Arakawa, Y. (2019) · Journal of Information Processing · doi:10.2197/ipsjjip.27.614 · Cited by 13
Cornet, V. P., Daley, C. N., Cavalcanti, L., Parulekar, A., & Holden, R. J. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-816427-3.00014-2 · Cited by 13
Self-citationSelby, P. L., Vojtila, L., Ashfaq, I., Dragonetti, R., Melamed, O. C., Carriere, R., LaChance, L., Kohut, S. A., Hahn, M., & Mulsant, B. H. (2020) · Early Intervention in Psychiatry · doi:10.1111/eip.13018 · Cited by 13
Outliers in Smartphone Sensor Data Reveal Outliers in Daily Happiness
Buda, T. S., Khwaja, M. K., & Matic, A. (2021) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3448095 · Cited by 13
Gouda, A. D. K., Sorour, M. S., Ayoub, A. S., Aboushady, R. M. N., Awad, M., swerky, F. M. E., Ayed, M. M. A., Elrefay, B. W., Elshnawie, H. A. E., & Elsayed, H. T. M. (2026) · BMC Nursing · doi:10.1186/s12912-025-04238-8 · Cited by 12
Twelve Smartphone Applications for Health Management of Older Adults during the COVID-19 Pandemic
Ha, S. K., Lee, H. S., & Park, H. Y. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph181910235 · Cited by 12
Goulding, E. H., Dopke, C. A., Rossom, R. C., Michaels, T., Martin, C. R., Ryan, C., Jonathan, G., McBride, A., Babington, P., Bernstein, M., et al. (2021) · JMIR Research Protocols · doi:10.2196/30710 · Cited by 13
Examining and Promoting Explainable Recommendations for Personal Sensing Technology Acceptance
Newn, J., Kelly, R., D’Alfonso, S., & Lederman, R. (2022) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3550297 · Cited by 15
A software development for medical with a multiple decision taking functionalities
Butt, S. A., Khalid, A., & Ali, A. (2022) · Advances in Engineering Software · doi:10.1016/j.advengsoft.2022.103294 · Cited by 12
Fontecha, J., Gónzalez, I., & Bravo, J. (2019) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-019-01369-0 · Cited by 12
An Intelligent Cloud-Based Platform for Effective Monitoring of Patients with Psychotic Disorders
Maglogiannis, I. G., Zlatintsi, A., Menychtas, A., Papadimatos, D., Filntisis, P. P., Efthymiou, N., Retsinas, G., Tsanakas, P. D., & Maragos, P. A. (2020) · IFIP advances in information and communication technology · doi:10.1007/978-3-030-49186-4_25 · Cited by 12
Smartphone-based Mental State Estimation: A Survey from a Machine Learning Perspective
Fukazawa, Y., Yamamoto, N., Hamatani, T., Ochiai, K., Uchiyama, A., & Ohta, K. (2020) · Journal of Information Processing · doi:10.2197/ipsjjip.28.16 · Cited by 13
Rottstädt, F., Becker, E., Wilz, G., Croy, I., Baumeister, H., & Terhorst, Y. (2024) · Frontiers in Digital Health · doi:10.3389/fdgth.2024.1335776 · Cited by 11
Yu, J., Chiu, C., Wang, Y., Dzubur, E., Lu, W., & Hoffman, J. E. (2021) · Journal of Medical Internet Research · doi:10.2196/27709 · Cited by 11
Yoo, D. W., Bhatnagar, A., Ernala, S. K., Ali, A. F., Birnbaum, M. L., Abowd, G. D., & Choudhury, M. D. (2023) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3579479 · Cited by 11
Jacobucci, R., Ammerman, B. A., & Ram, N. (2024) · JMIR Formative Research · doi:10.2196/55999 · Cited by 11
Liang, L., Wang, Y., Ma, H., Zhang, R., Liu, R., Zhu, R., Zheng, Z., Zhang, X., & Wang, F. (2024) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2024.1422020 · Cited by 11
Are Machine Learning Methods the Future for Smoking Cessation Apps?
Abo-Tabik, M., Benn, Y., & Costen, N. P. (2021) · Sensors · doi:10.3390/s21134254 · Cited by 15
Kong, D., Fu, J., Hong, Y. A., Liu, S., & Luo, Y. (2022) · Iranian Journal of Public Health · doi:10.18502/ijph.v51i4.9233 · Cited by 11
Perspectives of Individual-Worn Sensors Assessing Personal Environmental Exposure
Schlink, U., & Ueberham, M. (2020) · Engineering · doi:10.1016/j.eng.2020.07.023 · Cited by 11
Control charts based on fuzzy costs for monitoring short autocorrelated time series
Hryniewicz, O., Kaczmarek‐Majer, K., & Opara, K. R. (2019) · International Journal of Approximate Reasoning · doi:10.1016/j.ijar.2019.08.013 · Cited by 11
DL4DED: Deep Learning for Depressive Episode Detection on Mobile Devices
Mdhaffar, A., Cherif, F., Kessentini, Y., Maalej, M., Thabet, J. B., Maalej, M. A., Jmaïel, M., & Freisleben, B. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-32785-9_10 · Cited by 12
Acceptance of smart sensing: a barrier to implementation—results from a randomized controlled trial
Terhorst, Y., Weilbacher, N., Suda, C., Simon, L., Messner, E.-M., Sander, L. B., & Baumeister, H. (2023) · Frontiers in Digital Health · doi:10.3389/fdgth.2023.1075266 · Cited by 10
From GPS to semantic data: how and why—a framework for enriching smartphone trajectories
Ibrahim, A., Zhang, H., Clinch, S., & Harper, S. (2021) · Computing · doi:10.1007/s00607-021-00993-z · Cited by 10
Mobile sensing in psychological and educational research: Examples from two application fields
Birtwistle, E., Schoedel, R., Bemmann, F., Wirth, A., Sürig, C., Stachl, C., Bühner, M., & Niklas, F. (2022) · International Journal of Testing · doi:10.1080/15305058.2022.2036160 · Cited by 10
Medich, M., Cannedy, S., Hoffmann, L., Chinchilla, M., Pila, J. M., Chassman, S., Calderon, R. A., & Young, A. S. (2023) · JMIR Human Factors · doi:10.2196/46909 · Cited by 12
HEAR4Health: a blueprint for making computer audition a staple of modern healthcare
Triantafyllopoulos, A., Kathan, A., Baird, A., Christ, L., Gebhard, A., Gerczuk, M., Karas, V., Hübner, T., Jing, X., Liu, S., et al. (2023) · Frontiers in Digital Health · doi:10.3389/fdgth.2023.1196079 · Cited by 12
Advanced Smartphone-Based Sensing with Open-Source Task Automation
Ueberham, M., Schmidt, F., & Schlink, U. (2018) · Sensors · doi:10.3390/s18082456 · Cited by 10
Craven, M. P., Andrews, J. A., Lang, A. R., Simblett, S. K., Bruce, S., Thorpe, S., Wykes, T., Morriss, R. K., & Hollis, C. P. (2020) · JMIR Formative Research · doi:10.2196/22756 · Cited by 10
Integration of Sensor-Based Technology in Mental Healthcare: A Systematic Scoping Review
Bosch, R., Noordzij, M. L., Westerhof, G. J., & Bohlmeijer, E. T. (2024) · Journal of Technology in Behavioral Science · doi:10.1007/s41347-024-00427-5 · Cited by 9
Terhorst, Y., Meßner, E.-M., Asare, K. O., Montag, C., Kannen, C., & Baumeister, H. (2024) · Journal of Medical Internet Research · doi:10.2196/55308 · Cited by 10
Plaitano, E. G., McNeish, D., Bartels, S. M., Bell, K. R., Dallery, J., Grabinski, M. J., Kiernan, M., Lavoie, H. A., Lemley, S. M., Lowe, M. R., et al. (2025) · Frontiers in Digital Health · doi:10.3389/fdgth.2025.1467772 · Cited by 10
Mansouri, S., Alharbi, Y., Alqahtani, A., Rokbani, N., & Mohammed, J. S. (2025) · Microchemical Journal · doi:10.1016/j.microc.2025.113093 · Cited by 9
Conversational agents on smartphones and the web
Bickmore, T., & O’Leary, T. K. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-90045-4.00010-1 · Cited by 9
Smartphone-Based Passive Sensing in Monitoring Patients With Cancer: A Systematic Review
Stuijt, D. G., Radanović, I., Kos, M., Schoones, J. W., Stuurman, F. E., Exadaktylos, V., Bins, A. D., Bosch, J. J., & van Oijen, M. G. H. (2023) · JCO Clinical Cancer Informatics · doi:10.1200/cci.23.00141 · Cited by 10
Simon, L., Terhorst, Y., Cohrdes, C., Pryss, R. C., Steinmetz, L., Elhai, J. D., & Baumeister, H. (2024) · Sleep Medicine X · doi:10.1016/j.sleepx.2024.100114 · Cited by 9
Rose, K. M., Lach, J., Perkhounkova, Y., Gong, J., Dandu, S. R., Dickerson, R. F., Emi, I. A., Fan, D., Specht, J. P., & Stankovic, J. A. (2018) · Journal of Gerontological Nursing · doi:10.3928/00989134-20180626-03 · Cited by 9
Making the MOST out of smartphone opportunities for mental health
D’Alfonso, S., Carpenter, N., & Álvarez‐Jiménez, M. (2018) · Australian Conference on Computer-Human Interaction (OzCHI) · doi:10.1145/3292147.3292230 · Cited by 9
It's time to bring human factors to primary care policy and practice
Beasley, J. W., Holden, R. J., Ötleş, E., Green, L. A., Steege, L. M. B., & Wetterneck, T. B. (2020) · Applied Ergonomics · doi:10.1016/j.apergo.2020.103077 · Cited by 9
Self-citationPoudyal, A., van Heerden, A. C., Hagaman, A. K., Islam, C., Thapa, A., Maharjan, S. M., Byanjankar, P., & Kohrt, B. A. (2021) · Frontiers in Public Health · doi:10.3389/fpubh.2021.633606 · Cited by 10
Mobile Sensing around the Globe
Phan, L. V., Modersitzki, N., Gloystein, K. K., & Müller, S. R. (2023) · Cambridge University Press eBooks · doi:10.1017/9781009099813.009 · Cited by 9
Commentary: Pediatric Digital Health Supported by the National Institutes of Health
Riley, W. T., Oh, A., Aklin, W. M., Sherrill, J. T., Wolff‐Hughes, D. L., Diana, A., Griffin, J. A., & Campo, R. (2018) · Journal of Pediatric Psychology · doi:10.1093/jpepsy/jsy108 · Cited by 9
Artificial Intelligence and Predictive Modeling in Mental Health
Hilty, D. M., Cheng, Y., & Luxton, D. D. (2024) · Springer eBooks · doi:10.1007/978-3-031-59936-1_13 · Cited by 12
Metadata
- Authors
- Cornet, V. P., & Holden, R. J.
- Affiliations
- Indiana University Indianapolis (US)
- Regenstrief Institute (US)
- Corresponding author
- Holden, Richard J.
- Venue
- Journal of Biomedical Informatics
- Publisher
- Elsevier BV
- Published
- 2018-01
- Volume
- 77
- Pages
- 120-132
- ISSN
- 1532-0464 (ISSN-L)
- PMC
- PMC5793918
- OpenAlex
- W2775819279
- Cited by
- 406
- References
- 90
- Citations by year
- 2026: 50 · 2025: 52 · 2024: 51 · 2023: 49 · 2022: 55 · 2021: 52 · 2020: 48 · 2019: 29 · 2018: 20
- FWCI
- 4.72
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Journal of Biomedical Informatics (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Purdue University Indianapolis (Indiana University) (submittedVersion)
- Funders
- National Institute on Aging
- National Institutes of Health (K01AG044439)
- Topics
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Keywords
- health monitoring, well-being, smartphone sensors, accelerometry, location data, audio data, usage data, bipolar disorder, schizophrenia, depression, older adults, privacy, clinical integration, personalized interventions
- MeSH terms
- Behavior Observation Techniques, Data Accuracy, Smartphone, Confidentiality, Health Status, Humans, Mental Health, Health Behavior, Monitoring, Ambulatory, Mobile Applications
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
